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Simplify Big Data Or It'll Be Useless For Sales

It’s been a hectic day so far for Maria. She closed a sale first thing this morning, but since then she’s hasn’t made much progress following up leads. That’s because she’s been inundated with a raft of disconnected but detailed analyses of new micromarket sales opportunities, obscure tailored pricing guidelines across dozens of customer and product variables, and social media reports laying out “hot leads” delivered in spreadsheets.

Why can’t this “big data”, which senior management are evangelical about, actually help her rather than making her job harder?

Good question. Many companies have grasped that today’s explosion of data and analytics can help them grow. But they haven’t realized that if the sales force can’t use the findings, then it’s a waste of time and effort. The key to making complex data analysis work is to make it simple for sales.

Fortunately, as we learned in writing the book Sales Growth, some companies have got it right. From their experiences, we’ve found three lessons for how to keep it simple.

Simple leads. In a world of big data, sales targets and leads can proliferate but they are of no use if they just create a morass of information for the sales person to wade through. Then you just have data without insight, and that makes Maria’s job more complicated. The beauty of big data is that is allows for precise micro-segmenting so that each individual lead generated can be directed to the right salesperson. So, rather than big data being something that Maria has to decipher, the findings instead are embedded into the sales process and fed straight to her. One tech company, for example, set up a real-time sales lead operation by first identifying a number of relevant keywords and question phrases that signaled potential sales opportunities online. Then it analyzed in real time the social media data on Twitter, Quora, etc. Data analysts tracked purchase decision makers and influencers, asking questions that incorporated one of the select keywords, matching the buyer’s question and location with the company’s internal data to pinpoint specific hunting opportunities. That lead was then sent to the rep covering that particular company, with a simplified set of insights around the company’s questions. Sales reps converted these solid leads almost 80 percent of the time.

Simple tests. A top sales organization isn’t staffed by robots. People have intuition and gut feelings based on experience and talent. Data should support those sparks in your sales force rather than quashing them. Use the data to develop and track simple tests for your hunches to see if you’re on the right track. It may even be preferable to use third-party analysts, who can generate ideas in days and weeks rather than waiting to build your own in-house capabilities. By all means try and uncover useful patterns by analyzing data, but do it fast and take an experimental approach. Test any findings on a sample group to see whether the impact is worth building on. Speed is of the essence here – especially in fast-moving competitive environments. If teams are mired in data analysis searching for the absolute best solution they will never deliver any benefits at all. Worse still, without any quick wins from pilots, senior management will be unconvinced of the merits of the whole approach.

Simple tools. Sophisticated algorithms, databases, data warehouses, and computations help determine insights. But Maria needs to be able to see the wood for the trees. Sales organizations have to be able to mask all the complexity so sales leaders can take action. A cargo airline recognized it needed to improve its daily sales decisions for its key accounts, such as how much capacity to allocate to each customer. The variances in time of day, day of week, flight space availability, etc. were just too many for sales staff to handle. The company therefore developed a complex model that took all the frequently changing dynamics of the cargo industry into account, as well as opportunities for different negotiation strategies based on supply and demand. But that wasn’t the win. The company then took all that complexity and hid it behind a simple “dashboard”, which it gave to the sales force. This dashboard provided simple guidelines on flight capacity, corresponding pricing, as well as competitor options. The result? A 20 percent boost in share of wallet.

It’s not enough to analyze Big data or even extract insights from them. The best sales leaders mask all that complexity and keep it simple for the front lines so sales staff like Maria can get on with what they do best – sales, not statistics.

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